Georgia all-cause, sex-specific mortality, ages 55-64, years 2014-2018

A simple features (sf) object for Georgia counties with sex- and age-specific deaths and populations at risk (2014-2018), plus select estimates (with standard errors) of county characteristics. Standard errors of the ICE were calculated using the Census Bureau's variance replicate tables.

georgia

Format

A simple features object with county geometries and the following columns:

GEOID: Six digit combined state and county FIPS code
NAME: County name
ALAND: Land area
AWATER: Water area
population: Census Bureau 2018 county population estimate
white: Percent White, ACS 2018 five-year estimate
black: Percent Black, ACS 2018 five-year estimate
hisp: Percent Hispanic/Latino, ACS 2018 five-year estimate
ai: Percent American Indian, ACS 2018 five-year estimate
deaths.male: Male deaths, 55-64 yo, 2014-2018
pop.at.risk.male: Population estimate, males, 55-64 yo, years 2014-2018 (total), ACS 2018 five-year estimate
pop.at.risk.male.se: Standard error of the pop.at.risk.male estimate
deaths.female: Female deaths, 55-64 yo, 2014-2018
pop.at.risk.female: Population estimate, females, 55-64 yo, years 2014-2018 (total), ACS 2018 five-year estimate
pop.at.risk.female.se: Standard error of the pop.at.risk.female estimate
ICE: Index of Concentration at the Extremes
ICE.se: Standard error of the ICE estimate, calculated using variance replicate tables
income: Median household income, ACS 2018 five-year estimate
income.se: Standard error of the income estimate
college: Percent of the population age 25 or higher than has a bachelors degree of higher, ACS 2018 five-year estimate
college.se: Standard error of the college estimate
insurance: Percent of the population with health insurance coverage, ACS 2018 five-year estimate
insurance.se: Standard error of the insurance estimate
rate.male: Raw (crude) age-specific male mortality rate, 2014-2018
rate.female: Raw (crude) age-specific female mortality rate, 2014-2018
geometry: simple features geometry for county boundaries

Source

Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999-2018 on CDC Wonder Online Database. 2020. Available online: http://wonder.cdc.gov (accessed on 19 October 2020).

Donegan, Connor and Chun, Yongwan and Griffith, Daniel A. (2021). ``Modeling community health with areal data: Bayesian inference with survey standard errors and spatial structure.'' Int. J. Env. Res. and Public Health 18 (13): 6856. DOI: 10.3390/ijerph18136856 Data and code: https://github.com/ConnorDonegan/survey-HBM.

Kyle Walker and Matt Herman (2020). tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames. R package version 0.11. https://CRAN.R-project.org/package=tidycensus

US Census Bureau. Variance Replicate Tables, 2018. Available online: https://www.census.gov/programs-surveys/acs/data/variance-tables.2018.html (accessed on 19 October 2020).

Examples

data(georgia)
head(georgia)

library(sf)
plot(georgia[,'rate.female'])